Background: Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented. Methods: We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses (...) from 172 UK laypeople, recruited through Prolific Academic. Findings: Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the “dehumanisation of healthcare” and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender. Conclusions: There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented. (shrink)

Cohen’s (2023) mapping exercise of possible bioethical issues emerging from the use of ChatGPT in medicine provides an informative, useful, and thought-provoking trigger for discussions of AI ethic...

In their proof-of-concept study, Meier et al. built an algorithm to aid ethical decision making. In the limitations section of their paper, the authors state a frequently cited ax...

BackgroundHealthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use.MethodsPubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was title and abstract (...) screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis.ResultsArtificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process.ConclusionsThe prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human–machine interaction, have been neglected in the debate on AI for clinical ethics so far.Trial registrationThis review is registered at Open Science Framework (https://osf.io/wvcs9). (shrink)

Meier et al. describe a pilot study that developed METHAD, an AI-based Medical Ethics Advisor tool that draws on the principlism approach and was tested using text-book cases and clinical et...

In their proof-of-concept, Meier and colleagues describe the purpose and programming decisions underpinning Medical Ethics Advisor, an automated decision support system used t...

Allowing algorithms to guide or determine decision-making in ethically complex situations, and eventually satisfying the need for good clinical ethics consultation work, is a philosophically intere...

The COVID-19 pandemic has been overwhelming public health-care systems around the world. With demand exceeding the availability of medical resources in several regions, hospitals have been forced to invoke triage. To ensure that this difficult task proceeds in a fair and organised manner, governments scrambled experts to draft triage guidelines under enormous time pressure. Although there are similarities between the documents, they vary considerably in how much weight their respective authors place on the different criteria that they propose. Since most (...) of the recommendations do not come with ethical justifications, analysing them requires that one traces back these criteria to their underlying theories of distributive justice. In the literature, COVID-19 triage has been portrayed as a value conflict solely between utilitarian and egalitarian elements. While these two accounts are indeed the main antipodes, I shall show that in fact all four classic theories of distributive justice are involved: utilitarianism, egalitarianism, libertarianism, and communitarianism. Detecting these in the documents and classifying the suggested criteria accordingly enables one to understand the balancing between the different approaches to distributive justice—which is crucial for both managing the current pandemic and in preparation for the next global health crisis. (shrink)

In their Target Article, Rahimzadeh et al. (2023) discuss the virtues and vices of employing ChatGPT in ethics education for healthcare professionals. To this end, they confront the chatbot with a moral dilemma and analyse its response. In interpreting the case, ChatGPT relies on Beauchamp and Childress’ four prima-facie principles: beneficence, non-maleficence, respect for patient autonomy, and justice. While the chatbot’s output appears admirable at first sight, it is worth taking a closer look: ChatGPT not only misses the point when (...) applying the principle of non-maleficence; its response also fails, in several places, to honour patient autonomy – a flaw that should be taken seriously if large language models are to be employed in ethics education. I therefore subject ChatGPT’s reply to detailed scrutiny and point out where it went astray. (shrink)

Can machine intelligence do clinical ethics? And if so, would applying it to actual medical cases be desirable? In a recent target article (Meier et al. 2022), we described the piloting of our advisory algorithm METHAD. Here, we reply to commentaries published in response to our project. The commentaries fall into two broad categories: concrete criticism that concerns the development of METHAD; and the more general question as to whether one should employ decision-support systems of this kind—the debate we set (...) out to ignite with our target article. (shrink)

In October 2021, the Allen Institute for Artificial Intelligence publicly released Delphi, an artificial intelligence system trained to make general moral decisions (Allen Institute for Artifi...

Machine learning and other forms of artificial intelligence can improve parts of clinical decision making regarding the gathering and analysis of data, the detection of disease, and the provis...

Lukas J. Meier et al. offer the promise of a pathway for resolving clinical bioethical problems using an artificial intelligence interface. The ultimate goal, we assume, is...

In their paper “Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept,” Meier et al. present the design and preliminary results of a proof-of-concept clinical ethics algor...

Answering the design questions inherent to building and deploying machine learning tools —based on algorithms that can learn from and make predictions on large data sets without being explicitl...

On reading “Algorithms for Ethical Decision-Making in the Clinical: A Proof of Concept,” I imagined that for some the fundamental problem with the authors' approach is the very...

Artificial intelligence has found its way into many areas of human life, serving a range of purposes. Sometimes AI tools are designed to help humans eliminate high-volume, tedious, routine tas...

When considering the introduction of AI to support medical decision-making, one must take an end-to-end, holistic approach to development, evaluation, integration and governance. (Cabitza and Zeito...

Although machine intelligence is increasingly employed in healthcare, the realm of decision-making in medical ethics remains largely unexplored from a technical perspective. We propose an approach based on fuzzy cognitive maps (FCMs), which builds on Beauchamp and Childress’ prima-facie principles. The FCM’s weights are optimized using a genetic algorithm to provide recommendations regarding the initiation, continuation, or withdrawal of medical treatment. The resulting model approximates the answers provided by our team of medical ethicists fairly well and offers a high degree (...) of interpretability. Possible applications of such a system include informal guidance on medical ethics dilemmas as well as educational purposes. (shrink)